Cultivation-free and sequencing-free protocol for the simultaneous detection and typing of Serratia marcescens: a rapid and cost-effective tool for large environmental/clinical screenings
Bibliographic record
Abstract
Serratia marcescens is an opportunistic pathogen able to cause severe and lethal infections.The bacterium is able to survive in inhospitable environments (e.g.soap dispenser) and to rapidly spread among patients, causing large outbreaks, in particular in Neonates Care Intensive Units (NICUs).Recent genomic studies revealed that most S. marcescens nosocomial infections are caused by a specific clinical-associated clone.The timely detection of this clone in environmental or clinical samples can drastically increase the efficiency of hospital surveillance programs.At the state of the art, Whole Genome Sequencing (WGS)-based typing is the only portable method able to identify this clinical-associated clone, but it requires days to obtain results.Here we present a cultivation-free Hypervariable-Locus Melting Typing (HLMT) protocol for the fast simultaneous detection and typing of S. marcescens, which can be performed using a common qPCR real-time instrument, in ~5 hours with a cost of ~5 dollars.The protocol showed 100% detection capability on mixed DNA samples, with a limit of detection of 10 genome copies.The typing capability was evaluated on a large dataset of isolates (n = 230) comparing WGS and HLMT typing results.The protocol was able to classify 85% isolates with a specificity of 0.96 and sensitivity of 0.97.Lastly, the portability among laboratories of the method has been assessed.This cultivation-free HLMT protocol is a cost and time saving method for S. marcescens detection and typing, suitable for large environmental/clinical surveillance screenings, also in low-middle income countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".